An inner approximation method to compute the weight set decomposition of a triobjective mixed-integer problem

This article is dedicated to the weight set decomposition of a multiobjective (mixed-)integer linear problem with three objectives. We propose an algorithm that returns a decomposition of the parameter set of the weighted sum scalarization by solving biobjective subproblems via Dichotomic Search whi...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of global optimization Jg. 77; H. 4; S. 715 - 742
Hauptverfasser: Halffmann, Pascal, Dietz, Tobias, Przybylski, Anthony, Ruzika, Stefan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY Springer US 01.08.2020
Springer
Springer Nature B.V
Springer Verlag
Schlagworte:
Algorithms
Analysis
Approximation
Computer Science
Decomposition
Exploration
Integers
Mathematical analysis
Mathematics
Mathematics and Statistics
Methods
Multiobjective optimization
Multiple objective analysis
Operations Research
Operations Research/Decision Theory
Optimization
Polynomials
Real Functions
Scalarization
Weight
Weight set decomposition
Weighted sum method
Scalarization
Weight set decomposition
Weighted sum method
Multiobjective optimization
ISSN:1573-2916, 0925-5001, 1573-2916
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This article is dedicated to the weight set decomposition of a multiobjective (mixed-)integer linear problem with three objectives. We propose an algorithm that returns a decomposition of the parameter set of the weighted sum scalarization by solving biobjective subproblems via Dichotomic Search which corresponds to a line exploration in the weight set. Additionally, we present theoretical results regarding the boundary of the weight set components that direct the line exploration. The resulting algorithm runs in output polynomial time, i.e. its running time is polynomial in the encoding length of both the input and output. Also, the proposed approach can be used for each weight set component individually and is able to give intermediate results, which can be seen as an “approximation” of the weight set component. We compare the running time of our method with the one of an existing algorithm and conduct a computational study that shows the competitiveness of our algorithm. Further, we give a state-of-the-art survey of algorithms in the literature.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1573-2916
0925-5001
1573-2916
DOI:10.1007/s10898-020-00898-9